Next Article in Journal
A Photovoltaic-Fed Z-Source Inverter Motor Drive with Fault-Tolerant Capability for Rural Irrigation
Previous Article in Journal
Non-Intrusive Identification of Load Patterns in Smart Homes Using Percentage Total Harmonic Distortion
Article

Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
*
Author to whom correspondence should be addressed.
Energies 2020, 13(18), 4629; https://doi.org/10.3390/en13184629
Received: 2 August 2020 / Revised: 22 August 2020 / Accepted: 3 September 2020 / Published: 6 September 2020
(This article belongs to the Section Wind, Wave and Tidal Energy)
Wind energy has been widely used in renewable energy systems. A probabilistic prediction that can provide uncertainty information is the key to solving this problem. In this paper, a short-term direct probabilistic prediction model of wind power is proposed. First, the initial data set is preprocessed by a box plot and gray correlation analysis. Then, a generalized method is proposed to calculate the natural gradient and the improved natural gradient boosting (NGBoost) model is proposed based on this method. Finally, blending fusion is used in order to enhance the learning effect of improved NGBoost. The model is validated with the help of measured data from Dalian Tuoshan wind farm in China. The results show that under the specified confidence, compared with the single NGBoost metamodel and other short-term direct probability prediction models, the model proposed in this paper can reduce the forecast area coverage probability while ensuring a higher average width of prediction intervals, and can be used to build new efficient and intelligent energy power systems. View Full-Text
Keywords: wind power; short-term direct probability prediction; improved natural gradient boosting; blending fusion wind power; short-term direct probability prediction; improved natural gradient boosting; blending fusion
Show Figures

Graphical abstract

MDPI and ACS Style

Li, Y.; Wang, Y.; Wu, B. Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting. Energies 2020, 13, 4629. https://doi.org/10.3390/en13184629

AMA Style

Li Y, Wang Y, Wu B. Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting. Energies. 2020; 13(18):4629. https://doi.org/10.3390/en13184629

Chicago/Turabian Style

Li, Yonggang, Yue Wang, and Binyuan Wu. 2020. "Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting" Energies 13, no. 18: 4629. https://doi.org/10.3390/en13184629

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop